Outlier detection for identification of anomalous cross-attribute clusters

ABSTRACT

A method of identifying regions in a subsurface that may be a hydrocarbon reservoir, the method including: extracting features from cross-attribute clusters; assigning a distance metric and linkage criterion in feature space; calculating, with a computer, a degree of anomaly for the cross-attribute clusters in the feature space; ranking the cross-attribute clusters in accordance with the degree of anomaly; and prospecting for hydrocarbons by investigating a subsurface region in accordance with the rankings.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/469,733 filed Mar. 10, 2017 entitled OUTLIERDETECTION FOR IDENTIFICATION OF ANOMALOUS CROSS-ATTRIBUTE CLUSTERS, theentirety of which is incorporated by reference herein.

The present application includes subject matter related to U.S. patentapplication Ser. No. 15/380,117, filed Dec. 15, 2016, published asUS2017/0192115 on Jul. 6, 2017, the entire contents of which are herebyincorporated by reference.

TECHNOLOGICAL FIELD

Exemplary embodiments described herein pertain generally to the field ofgeophysical prospecting, and more particularly to the identification ofregions in the subsurface that may be petroleum reservoirs.

BACKGROUND

This section is intended to introduce various aspects of the art, whichmay be associated with exemplary embodiments of the present invention.This discussion is believed to assist in providing a framework tofacilitate a better understanding of particular aspects of the presentinvention. Accordingly, it should be understood that this section shouldbe read in this light, and not necessarily as admissions of prior art.

Petroleum is found in underground reservoirs. The petroleum industryseeks to efficiently identify untapped petroleum reservoirs, which areoften also called ‘hydrocarbon’ reservoirs. Seismic data are often usedto help locate new hydrocarbon reservoirs. Seismic data are images ofthe subsurface that are made using the reflections of seismic waves thathave been propagated into the earth. In a typical seismic survey,seismic waves are generated by a source positioned at a desiredlocation. As the source generated waves propagate through thesubsurface, some of the energy reflects from subsurface interfaces andtravels back to the surface, where it is recorded by the receivers.

Seismic interpreters are geoscientists who use seismic data to helpdescribe layers and geologic structures in the subsurface. One ofseismic interpreters' primary jobs is to identify regions in thesubsurface that have a relatively high likelihood of being petroleumreservoirs. Conceptually, interpreters are trying to find trappedaccumulations of hydrocarbons. A schematic diagram of an exemplaryhydrocarbon reservoir 100 (vertically stripped region) is outlined inFIG. 1. The hydrocarbon reservoir 100 is an area under closure within asandy reservoir layer 102, enclosed by a sealing fault 104 and shaleylayers 101; the oil-water contact 103 is the lower boundary of thehydrocarbon accumulation.

In order to identify prospective regions, seismic interpreters combthrough and compare many different seismic datasets, which are alsoknown as seismic data ‘volumes’ because the dataset stores informationabout a three-dimensional volume under the surface of the Earth. As theseismic interpreters review the multiple seismic data volumes they lookfor subtle variations that indicate changes in rock and fluid propertiesof the subsurface material. Sometimes, seismic data are reprocessed togenerate seismic attribute volumes. A seismic attribute is a piece ofdata that is associated with a certain feature of the subsurface (e.g.,fluid content, rock texture, geometry of an interface between twodifferent types of rock; refer to FIGS. 2A-C). More particularly, aseismic attribute, sometimes referred to as a “feature” in computervision and pattern recognition literature, is a measureable property orderivative of seismic data used to highlight or identify areas orobjects of geological or geophysical interest (e.g., the presence ofhydrocarbons). These attributes are based on different characteristicsof component seismic datasets. Attributes often correlate with somephysical property of interest (e.g., acoustic impedance) and helpgeoscientists to see patterns that otherwise might go unnoticed. Suchattributes can represent a transformation of seismic data to a form thatis more useful to guide the search for hydrocarbon accumulations orindicate conditions that are potentially favorable to the accumulationof hydrocarbons. Different seismic attribute datasets are generated byprocessing the raw seismic reflection data in different ways.

In regions of the subsurface that are likely to be hydrocarbonreservoirs, seismic attributes have specific, geologically relevantspatial and contextual relationships. Building upon the example of theschematic attributes shown in FIGS. 2A-C, in a location where ahydrocarbon reservoir exists, we would expect to see reservoir rock(FIG. 2A) spatially coinciding with an attribute that reflects a changein fluid content (FIG. 2B); we would further expect one or two flatevents (FIG. 2C) within the boundary of the reservoir, corresponding tothe locations of the gas-oil fluid contact and/or the oil-water fluidcontact. These geologically relevant spatial relationships are shownschematically in FIG. 3, in which a likely location of a hydrocarbonreservoir is identified by circle 301.

Standard methods for identification of prospective hydrocarbonreservoirs using multiple (≥2) seismic attribute datasets are time andwork-intensive. An interpreter must manually scroll through multipleattribute datasets, each of which is extremely large (up to 100 GBs). Asthe interpreter reviews works, he or she must keep in mind subtlespatial variation in each of the many relevant attributes and mentallytrack the physical location and geologic context of all attributeobjects.

A previously proposed automated approach is to use a graphical model asan integrator of different attribute data (see, for example, U.S. PatentApplication Publication No. 2014/0278115, titled “Context BasedGeo-Seismic Object Identification). Other methods for data fusion useweighted sums of normalized attribute values, such as the use of aBayesian Belief Network (BBN) (see, for example, U.S. Pat. No.7,743,006, titled “Bayesian network triads for geologic and geophysicalapplications”) but such methods require that features within differentseismic attribute volumes be spatially collocated.

A recent patent application titled “A Clustering Algorithm forGeoscience Data Fusion” (U.S. patent application Ser. No. 15/380,117,filed Dec. 15, 2016, published as U.S. Patent Application PublicationNo. 2017/0192115, the entirety of which is hereby incorporated byreference) describes a method for fusing multiple seismic attributesinto a set of ‘prospective’ objects in the subsurface. These attributesare called cross-attribute clusters. Knowing the clusters can shift theinterpreters' task from scanning through seismic volumes to screeningthe clusters. FIGS. 6A-6C in U.S. Publication No. 2017/0192115illustrates vertical cross-sections of seismic attributes generated froma synthetic seismic data volume imaging a hydrocarbon system containingchannelized-sand reservoirs. FIG. 6A illustrates Amplitude Strength (the‘anchor’ attribute); FIG. 6B illustrates Near-vs-faramplitude-vs-offset; and FIG. 6C illustrates a ‘flat event’ attributethat identifies areas in which local geometry is horizontal relative tothe Earth surface. In FIGS. 6A and B, darker shades indicate higherattribute values; in FIG. 6C, lighter/whiter color indicates higherattribute values. FIG. 7 in U.S. Publication No. 2017/0192115illustrates a vertical cross-section of fused cross-attribute clustersof reservoir rock objects, flat-event objects, and AVO objects. Thecross-section shown in this FIG. 7 is from the same volume but not thesame location as the cross-sections shown in FIGS. 6A-6C.

The speed and quality of the screening process can be improved if theclusters are ranked so that the interpreter views the most prospectiveones first. Common ranking systems might be to sort the clusters fromlargest to smallest or to sort based on a single measure of‘brightness’. A ranking system more consistent with the manualinterpreter approach is to rank based on degree of anomaly—with the mostanomalous being more highly ranked than the least anomalous.

SUMMARY

A method of identifying regions in a subsurface that may be ahydrocarbon reservoir, the method including: extracting features fromcross-attribute clusters; assigning a distance metric and linkagecriterion in feature space; calculating, with a computer, a degree ofanomaly for the cross-attribute clusters in the feature space; rankingthe cross-attribute clusters in accordance with the degree of anomaly;and prospecting for hydrocarbons by investigating a subsurface region inaccordance with the rankings.

The method can further include: creating a hierarchical cluster treewith hierarchical agglomerative clustering and the distance metric andlinkage criterion; a first cutting of the tree at a highest branch sothat there are two clusters, each of which includes one or morecross-attribute clusters, the two clusters include all of thecross-attribute clusters and assigning a score to a smaller of the twoclusters; a second cutting of the tree at one branch lower than thehighest branch, so that there are three clusters including the twoclusters from the first cutting, the smaller of the two clusters fromthe first cutting retains the score it was assigned and assigninganother score to two remaining clusters from the second cutting;repeatedly cutting the tree one branch lower than an immediatelypreceding cut, retaining scores from preceding cuts of the tree, andassigning scores to smallest new clusters until a predetermined stoppingcriteria is met; and assigning any remaining unscored cross-attributeclusters a score.

In the method, the distance metric can be Euclidian distance, city-blockdistance, or Chebychev distance.

In the method, the linkage criteria can be farthest distance, shortestdistance, or average distance.

In the method, the hierarchical cluster tree can be a dendrogram.

The method can further include: performing a seismic acquisition,results from which are used to generate the cross-attribute clusters;identifying subsurface regions that may be a hydrocarbon reservoir,wherein the subsurface regions are identified from the ranking of thecross-attribute clusters; and drilling a well for extractinghydrocarbons from the hydrocarbon reservoir.

BRIEF DESCRIPTION OF THE DRAWINGS

While the present disclosure is susceptible to various modifications andalternative forms, specific example embodiments thereof have been shownin the drawings and are herein described in detail. It should beunderstood, however, that the description herein of specific exampleembodiments is not intended to limit the disclosure to the particularforms disclosed herein, but on the contrary, this disclosure is to coverall modifications and equivalents as defined by the appended claims. Itshould also be understood that the drawings are not necessarily toscale, emphasis instead being placed upon clearly illustratingprinciples of exemplary embodiments of the present invention. Moreover,certain dimensions may be exaggerated to help visually convey suchprinciples.

FIG. 1 is a schematic diagram of an exemplary hydrocarbon reservoir.

FIGS. 2A, 2B and 2C illustrate examples of seismic attributes.

FIG. 3 is a schematic diagram of a hypothetical hydrocarbon reservoirand the corresponding spatial relationships between the seismicattributes schematically diagramed in FIGS. 2A, 2B, and 2C.

FIG. 4 illustrates a flow chart of seismic input to identification.

FIG. 5 illustrates a flow chart of how to rank clusters.

FIG. 6 illustrates a dendrogram for data contained in Table 1.

FIG. 7 is a scatter plot, or bubble chart, graphically depicting datacontained in Table 1.

FIG. 8 is an exemplary computer upon which the present technologicaladvancement may be implemented.

DETAILED DESCRIPTION

Exemplary embodiments are described herein. However, to the extent thatthe following description is specific to a particular, this is intendedto be for exemplary purposes only and simply provides a description ofthe exemplary embodiments. Accordingly, the invention is not limited tothe specific embodiments described below, but rather, it includes allalternatives, modifications, and equivalents falling within the truespirit and scope of the appended claims.

The present technological advancement is a method for automaticallyranking the cross-attribute clusters/prospective objects by degree ofanomaly. Because objects that are most anomalous are called outliers,the present technological advancement can be based on an outlierdetection algorithm. The method is robust to noisy and missing data, andit is easily modifiable when different attributes are used in theanalysis. The method is efficient and scalable because operations can bedone at the cluster level, not the pixel level.

A method to rank cross-attribute clusters by degree of anomaly can beone component of a larger process as illustrated in FIG. 4. For brevity,and to prevent overloading the word ‘cluster’, cross-attribute clustersare henceforth referred to as objects. Step 401 can include inputting aseismic volume. Step 402 can include calculating seismic attributes.Step 403 can include fusing seismic attributes. Step 404 can includeranking clusters by degree of anomaly. Step 405 can include identifyingpotential hydrocarbons.

FIG. 5 illustrates an exemplary method of implementing step 404. Step501 can include extracting features (or statistics) from each object.Step 502 can include assigning a distance metric and linkage criterionin the feature space. Step 503 can include calculating a degree ofanomaly using a hierarchical agglomerative clustering algorithm. Step504 can include outputting a ranked list of objects for identificationof potential hydrocarbons.

The method illustrated in FIG. 5 can have one input (a set of N objectseach composed of M attributes) and one output (a list of objects rankedby degree of anomaly).

Step 501 can include defining and extracting n features from all theobjects. Features can be defined manually by the analyst orautomatically using unsupervised machine learning techniques includingbut not limited to principal component analysis and convolutional neuralnetworks. Because there are N objects and n features for each, there area total of N*n features to be extracted. The effect of featuredefinition and extraction is data reduction—reducing each object frompotentially millions of pixels in a seismic volume to n features.

Step 502 can include defining a distance metric and linkage criterion inthe feature space. In the context of hierarchical agglomerativeclustering, the distance metric measures the distance between eachobject in the feature space (see, FIG. 7). Each object can be a point infeature space. As an example, one can consider the four dimensionalspace illustrated in FIG. 7. There are 7 points on the graph; each iscompletely described by a four dimensional vector. The distance metricdefines how one can measure the distance between each four dimensionalvector. The linkage criterion measures the distance between sets (orclusters) of objects in the feature space. Once one has clustered someobjects together (look at group of 701 in FIG. 7 and group 702 in FIG.7), one needs to measure the distance between the clusters. In otherwords, what is the distance between the group 701 and group 702 in FIG.7. There are several ways to do this: 1) one could take the distancebetween the two nearest objects in each set, 2) the two farthest objectsin each set, 3) the distance between centers of the sets, etc.

Distance metrics include but are not limited to the Euclidean distance,city-block distance, and Chebychev distance. However, other distancemetrics can be used. For features that are non-numeric, distance metricsinclude but are not limited to the Hamming distance and Jaccarddistance. Linkage criteria include are but are not limited to thefarthest distance, the shortest distance, and the average distance.

Step 503 can include calculating the degree of anomaly of each object inthe feature space. This can be done through the following substeps:

-   -   (a) Create a hierarchical cluster tree using hierarchical        agglomerative clustering with the metric and linkage functions        defined in Step 502. The tree can be displayed in a dendrogram        (see FIG. 6);    -   (b) Cut the tree at its highest branch so that there are two        clusters. Each cluster contains one or more objects, and the two        clusters together contain all objects. Assign a score of 1 to        all objects in the cluster that is smaller;    -   (c) Cut the tree one branch lower so that there are three        clusters. The smaller cluster from the previous step remains the        same and retains the score of 1. The larger cluster from the        previous step is now two clusters. Assign a score of 2 to all        objects in the smaller of these two clusters;    -   (d) Repeatedly cut the tree lower, retain scores from the        previous steps, and assign an incremental score (3, 4, etc.) to        the smallest, new cluster. This process stops when a stopping        criterion is met. Stopping criteria include but are not limited        to i) specifying the maximum number of objects that can be        assigned a score, ii) specifying the maximum percentage of        objects that can be assigned a score, and iii) specifying an        inconsistency coefficient for linkage heights; and    -   (e) Assign all remaining unscored objects an incremental integer        score.

Step 504 can include outputting a ranked list of objects for analysisand identification of potential hydrocarbons. The ranked list can followfrom the degree of anomaly scored received in step 503. Those objectswith a score of 1 are the most anomalous since they are the last to jointhe larger group; hence they are the first in the ranked list ofobjects. They are followed by those objects that received a score of 2,3, and so on.

The following is an example of an application of the presenttechnological advancement. While the example may be simplified forexplanation purposes, the present technological advancement is certainlyapplicable to more complex scenarios.

For purposes of the example, there are seven objects and the objects arecross-attribute clusters where the attributes are an interior attribute,a flat event attribute, and an AVO attribute.

In the example, four features are manually defined per object (see step501). The features are size of the interior reservoir attribute, averageamplitude of the flat event attribute, and average amplitude of the AVOattribute. Table 1 contains feature data for the seven objects.

TABLE 1 Raw (synthetic) data for the example. There are seven objects(cross- attribute clusters) and four features. Feature 2 Feature 3Feature 4 Feature 1 (Reservoir (Flat Event (AVO (Size) Brightness)Brightness) Brightness) Object 1 10 0.10 0.10 0.10 Object 2 9 0.15 0.050.12 Object 3 12 0.08 0.10 0.15 Object 4 15 0.12 0.12 0.08 Object 5 320.55 0.30 0.28 Object 6 36 0.50 0.25 0.35 Object 7 70 0.80 0.82 0.78

FIG. 7 illustrates the data of Table 1 graphically in a bubble chart.Based on FIG. 7, it is visually evident that there is one extremelyanomalous object and two mildly anomalous objects.

Corresponding to step 502, the Euclidean distance metric between each ofthe 7 objects and shortest distance linkage is assigned.

A hierarchical cluster tree (see FIG. 6) is created (see step 503).Cutting this tree at the highest level yields two clusters. The smallestcluster contains only one object (object 7). Thus, object 7 is assigneda score of 1. The smaller of the two clusters contains twoobject—objects 5 and 6. Using a stopping criterion stating that at most50% of the objects can be assigned a score, the process is stopped. Allremaining objects are given a score of 3.

The objects can be ordered based on their score and placed into table 2for the ranked list of objects (see step 504). Object 7, with a score of1, is the most anomalous object. Objects 5 and 6, with a score of 2, arethe next most anomalous. Objects, 1, 2, 3, and 4, with a score of 3, aredeemed not anomalous.

TABLE 2 Ranked list of objects (cross-attribute clusters) associatedwith Step 504. Ranked List Object 7 1 Object 5 2 Object 6 2 Object 1 3Object 2 3 Object 3 3 Object 4 3

Outlier Detection for Identification of Anomalous Cross-AttributeClusters can rank objects (originally cross-attribute clusters) by thedegree of their anomaly. The present technological advancement increasesthe effectiveness and efficiency of seismic interpreters, allowing themto quickly screen prospective regions in a seismic volume. The presenttechnological advancement is fast (i.e., improves operation of thecomputer) since it operates at the object level—not the pixel level. Thepresent technological advancement is easily adaptable to new featuresoriginating from an unsupervised machine learning algorithm or newfeatures originating from new attributes.

Furthermore, the cross-attribute clusters and their rankings generatedby the present technological advancement can be used to managehydrocarbons. As used herein, hydrocarbon management includeshydrocarbon extraction, hydrocarbon production, hydrocarbon exploration,identifying potential hydrocarbon resources, identifying well locations,determining well injection and/or extraction rates, identifyingreservoir connectivity, acquiring, disposing of and/or abandoninghydrocarbon resources, reviewing prior hydrocarbon management decisions,and any other hydrocarbon-related acts or activities.

FIG. 8 is a block diagram of a computer system 2400 that can be used toexecute the present techniques. A central processing unit (CPU) 2402 iscoupled to system bus 2404. The CPU 2402 may be any general-purpose CPU,although other types of architectures of CPU 2402 (or other componentsof exemplary system 2400) may be used as long as CPU 2402 (and othercomponents of system 2400) supports the operations as described herein.Those of ordinary skill in the art will appreciate that, while only asingle CPU 2402 is shown in FIG. 8, additional CPUs may be present.Moreover, the computer system 2400 may comprise a networked,multi-processor computer system that may include a hybrid parallelCPU/GPU 2414 system. The CPU 2402 may execute the various logicalinstructions according to various teachings disclosed herein. Forexample, the CPU 2402 may execute machine-level instructions forperforming processing according to the operational flow described.

The computer system 2400 may also include computer components such asnontransitory, computer-readable media. Examples of computer-readablemedia include a random access memory (RAM) 2406, which may be SRAM,DRAM, SDRAM, or the like. The computer system 2400 may also includeadditional non-transitory, computer-readable media such as a read-onlymemory (ROM) 2408, which may be PROM, EPROM, EEPROM, or the like. RAM2406 and ROM 2408 hold user and system data and programs, as is known inthe art. The computer system 2400 may also include an input/output (I/O)adapter 2410, a communications adapter 2422, a user interface adapter2424, and a display adapter 2418.

The I/O adapter 2410 may connect additional non-transitory,computer-readable media such as a storage device(s) 2412, including, forexample, a hard drive, a compact disc (CD) drive, a floppy disk drive, atape drive, and the like to computer system 2400. The storage device(s)may be used when RAM 2406 is insufficient for the memory requirementsassociated with storing data for operations of the present techniques.The data storage of the computer system 2400 may be used for storinginformation and/or other data used or generated as disclosed herein. Forexample, storage device(s) 2412 may be used to store configurationinformation or additional plug-ins in accordance with the presenttechniques. Further, user interface adapter 2424 couples user inputdevices, such as a keyboard 2428, a pointing device 2426 and/or outputdevices to the computer system 2400. The display adapter 2418 is drivenby the CPU 2402 to control the display driver 2416 and the display on adisplay device 2420 to, for example, present information to the userregarding available plug-ins.

The architecture of system 2400 may be varied as desired. For example,any suitable processor-based device may be used, including withoutlimitation personal computers, laptop computers, computer workstations,and multi-processor servers. Moreover, the present technologicaladvancement may be implemented on application specific integratedcircuits (ASICs) or very large scale integrated (VLSI) circuits. Infact, persons of ordinary skill in the art may use any number ofsuitable hardware structures capable of executing logical operationsaccording to the present technological advancement. The term “processingcircuit” encompasses a hardware processor (such as those found in thehardware devices noted above), ASICs, and VLSI circuits. Input data tothe computer system 2400 may include various plug-ins and library files.Input data may additionally include configuration information.

The present techniques may be susceptible to various modifications andalternative forms, and the examples discussed above have been shown onlyby way of example. However, the present techniques are not intended tobe limited to the particular examples disclosed herein. Indeed, thepresent techniques include all alternatives, modifications, andequivalents falling within the spirit and scope of the appended claims.

REFERENCES

-   The following publications are hereby incorporated by reference in    their entirety:-   Ester, Martin; Kriegel, Hans-Peter; Sander, Jorg; Xu, Xiaowei    (1996), A Density-Based Algorithm for Discovering Clusters in Large    Spatial Databases with Noise, Proc. Of 2nd Intl. Conf. on Knowledge    Discovery and Data Mining;-   Simoudis, Evangelos; Han, Jiawei; Fayyad, Usama M., eds. A    density-based algorithm for discovering clusters in large spatial    databases with noise. Proceedings of the Second International    Conference on Knowledge Discovery and Data Mining (KDD-96). AAAI    Press. pp. 226-231, ISBN 1-57735-004-9. CiteSeerX: 10.1.1.71.1980;-   MacQueen, J. B. (1967). Some Methods for classification and Analysis    of Multivariate Observations. Proceedings of 5th Berkeley Symposium    on Mathematical Statistics and Probability 1. University of    California Press. pp. 281-297. MR 0214227. Zbl 0214.46201, Retrieved    2009 Apr. 7;-   Hastie, T. et al, “The Elements of Statistical Learning—Data Mining,    Inference, and Prediction”, Second Edition, Springer, 2009, p. 520;-   Meagher, Donald (October 1980). “Octree Encoding: A New Technique    for the Representation, Manipulation and Display of Arbitrary 3-D    Objects by Computer”, Rensselaer Polytechnic Institute (Technical    Report IPL-TR-80-111); and-   Steinhaus, H. (1957). “Sur la division des corps matériels en    parties”. Bull. Acad. Polon. Sci. (in French) 4 (12): 801-804. MR    0090073, Zbl 0079.16403.

What is claimed is:
 1. A method of identifying regions in a subsurfacethat may be a hydrocarbon reservoir, the method comprising: extractingfeatures from cross-attribute clusters; assigning a distance metric andlinkage criterion in feature space; calculating, with a computer, adegree of anomaly for the cross-attribute clusters in the feature space,wherein the calculating comprises: creating a hierarchical cluster treewith hierarchical agglomerative clustering and the distance metric andlinkage criterion; a first cutting of the tree at a highest branch sothat there are two clusters, each of which includes one or morecross-attribute clusters, the two clusters include all of thecross-attribute clusters and assigning a score to a smaller of the twoclusters; a second cutting of the tree at one branch lower than thehighest branch, so that there are three clusters including the twoclusters from the first cutting, the smaller of the two clusters fromthe first cutting retains the score it was assigned and assigninganother score to two remaining clusters from the second cutting;repeatedly cutting the tree one branch lower than an immediatelypreceding cut, retaining scores from preceding cuts of the tree, andassigning scores to smallest new clusters until a predetermined stoppingcriteria is met; and assigning any remaining unscored cross-attributeclusters a score; ranking the cross-attribute clusters in accordancewith the degree of anomaly; and prospecting for hydrocarbons byinvestigating a subsurface region in accordance with the rankings. 2.The method of claim 1, wherein the distance metric is Euclidiandistance, city-block distance, or Chebychev distance.
 3. The method ofclaim 1, wherein linkage criteria is farthest distance, shortestdistance, or average distance.
 4. The method of claim 1, wherein thehierarchical cluster tree is a dendrogram.
 5. The method of claim 1,further comprising: performing a seismic acquisition, results from whichare used to generate the cross-attribute clusters; identifyingsubsurface regions that may be a hydrocarbon reservoir, wherein thesubsurface regions are identified from the ranking of thecross-attribute clusters; and drilling a well for extractinghydrocarbons from the hydrocarbon reservoir.